@ARTICLE{10.3389/fnins.2017.00313,
AUTHOR={Daniels, Bryan C. and Flack, Jessica C. and Krakauer, David C.},
TITLE={Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making},
JOURNAL={Frontiers in Neuroscience},
VOLUME={11},
PAGES={313},
YEAR={2017},
URL={https://www.frontiersin.org/article/10.3389/fnins.2017.00313},
DOI={10.3389/fnins.2017.00313},
ISSN={1662-453X},
ABSTRACT={A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a `coding duality' in which there are accumulation and consensus formation processes distinguished by different timescales.}
}